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Signal blind detection method based on complex sinusoidal chaotic neural network

A neural network, blind detection technology, applied in the direction of transmission modification based on link quality, device dedicated to receiver, shaping network in transmitter/receiver, etc., can solve the problem of slow energy function convergence speed, etc. Achieve the effect of excellent anti-noise performance, rich and flexible transient chaotic dynamic characteristics, and improved blind detection performance

Inactive Publication Date: 2017-05-03
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, TCNN has negative self-coupling, which will lead to slower convergence of the energy function

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  • Signal blind detection method based on complex sinusoidal chaotic neural network
  • Signal blind detection method based on complex sinusoidal chaotic neural network
  • Signal blind detection method based on complex sinusoidal chaotic neural network

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[0058] Below in conjunction with accompanying drawing, a kind of signal blind detection method based on compound sinusoidal chaotic neural network that the present invention proposes is described in detail:

[0059] A signal blind detection method based on compound sinusoidal chaotic neural network, its implementation process is as follows:

[0060] When ignoring noise, the receiver equation for a discrete-time channel is defined as

[0061] x N =SΓ T (1)

[0062] In the formula, X N is the received data array, S is the transmitted signal array, Γ is the channel impulse response h j Constituted block Toeplitz matrix; ( ) T Represents matrix transposition;

[0063] Among them, the sending signal array:

[0064] S=[s L+M (k),...,s L+M (k+N-1)] T =[s N (k),...,s N (k-M-L)] N×(L+M+1) ,

[0065] M is the channel order, L is the equalizer order, and N is the required data length;

[0066] the s L+M (k)=[s(k),...,s(k-L-M)] T , where s∈{±1}, moment k is a natural num...

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Abstract

The invention provides a signal blind detection method based on a complex sinusoidal chaotic neural network. A non-monotone excitation function combined by a complex sinusoidal chaotic neural network and Sigmoid, time-varying output function gains and a piecewise exponential annealing function are adopted to form a complex sinusoidal chaotic neural network; in case of each iteration, a chaotic neural network is entered firstly, and an activation function is then entered; being trapped in the local minimum can be prevented by the chaotic neural network. The method of the invention inherits the features of the chaotic neural network, and the blind detection performance is improved; the network has richer and more flexible transient chaos dynamics characteristics and stronger global searching capability; and in the same condition, the anti-noise performance is better than that of the traditional Hopfield signal blind detection algorithm.

Description

technical field [0001] The invention belongs to the technical field of wireless communication signal processing and neural network, in particular to a signal blind detection method based on compound sinusoidal chaotic neural network. Background technique [0002] The rapid development of data communication and wireless sensor network technology has put forward higher requirements for the blind detection of communication signals (BlindDetection). The so-called blind detection means that only the received signal itself can be used to detect the transmitted signal, thereby eliminating inter-symbol interference (ISI) to improve the information transmission rate and reliability. [0003] In order to solve the problems caused by various intelligent algorithms such as genetics, ant colony, immunity, and particle swarm, which are easy to fall into local minimum and slow convergence, many literatures have begun to use Hopfield neural network to study the problem of blind signal detec...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L1/00H04L25/03
CPCH04L1/0038H04L25/03165H04L2025/03464
Inventor 张昀李经纬于舒娟刘欢金超迪孟庆霞饶强
Owner NANJING UNIV OF POSTS & TELECOMM
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